Modelling for end of life care and long term conditions
1. Introduction This note provides a brief overview of the use of modelling to inform commissioning for end of life care and long term conditions. In recent years, two approaches have been used: The Cohort Model for end of life care has been developed using the ithink software by the Whole Systems Partnership; Long Term Conditions modelling has been undertaken using SIMUL8 software (SIMUL8 Professional and Scenario Generator). Both approaches adopt a ‘systems’ perspective and can be invaluable in helping commissioners and planners see the bigger picture and explore ‘what if’ scenarios.
2. Why use modelling? Modelling provides you with a simplified version of reality, that you can use to test out the impact of change - or, of course, of doing nothing. A model is a set of mathematical equations that work together to simulate what happens in your service, or your system, now, and then allows you to change some of the equations to see what will happen. Models are not designed to predict the future with certainty, or to replace good decisionmaking. But they can help you to explore ‘what if? …’ questions, such as:
What will the demand for my service be in X years’ time if the projected level of demographic change takes place in my population?
What will be the impact of increasing access to this service on the staffing requirements, outcomes and service costs?
If I invest (or disinvest) in this part of the current service, what will be the impact on the total patient pathway?
How much would I have to invest to meet access targets?
… or a combination of these Models can be designed around your particular population and current system, and can be as complex or as simple as you want. In both cases referred to in this paper the client has been involved in designing and developing the tools with the expert modelling being provided externally. Or, in the case of SIMUL8 projects, users have received training and support to use the software themselves. In both cases the goal has been to prepare a tool that can then be used, with support and local calibration, in local systems.
3. Modelling for end of life care and long term conditions The two modelling approaches outlined in the appendices to this paper each relate to a different aspect of this overall commissioning area. 1) The Cohort Model The Cohort Model was developed to explore how many people in a given population might be in the last year of life, and what the impact of increasing levels of identification of people in the last year of life could be on resources and workforce for end of life care. The development programme included extensive engagement with professionals across the East Midlands and also involved further workforce modelling through the application of the Skills for Health Functional Mapping toolkit. A version of the model is available free online, which demonstrates the model in action applied to a â€˜typicalâ€™ population of 200,000 in England: www.endoflifecare-intelligence.org.uk/end_of_life_care_models/cohort_model Four locations were given early adopter status as part of the model testing, which involved a brief engagement and model calibration process. Local versions of the model were then made available along with a bespoke report outlining population level targets for deaths in hospital alongside planning priorities that would be adopted to deliver these changes. 2) SIMUL8 and the Scenario Generator Scenario Generator is currently being used by seven pilot sites to simulate the QIPP Year of Care for Long Term Conditions. The simulation starts with the risk-stratified population, and their assessment of need, and dynamically models the flow of patients through different pathways of care depending on need and whether or not they are cared for by an integrated team. The simulation model allows users to change the health and social care services accessed by patients and the way in which they are used to reflect their local system. SIMUL8 software has also been used to simulate End of Life Care and to consider the interface between long term conditions and end of life services in West Kent. Distinctives and similarities These two modelling projects have been designed by separate teams and commissioned by different National Teams prior to the formation of NHSIQ. Distinctives and similarities can be understood as follows: The issue definition around which they have been designed has been different: the Cohort Model focuses on early recognition and the impact this has on realising choice of place of death, with consequences for financial and human resources; SIMUL8 modelling focuses on the likely demand from groups of patients with different needs, taking into account changing needs over time and services required, and how this impacts on activity volume, cost and capacity. Users can test service redesign solutions within financial envelopes.. Both model the consequences of these strategic goals.
The nature of the tools used is different in that the Cohort Model adopts a system dynamics approach where the tool deals with populations and averages whilst the Simulat8 software adopts a discrete event approach which simulates the flow of individual patients through a system drawn from a given population (which will have a particular need profile). Both approaches allow for the modelling of constraints and the impact on waiting times and resource utilisation. Both approaches can be helpful and relevant to commissioners looking to develop services for people with long term conditions and subsequent end of life care needs and will be complemented by local analysis of population needs and service utilisation.
4. Contacts The respective leads for the two modelling approaches are: The Cohort Model: firstname.lastname@example.org & SIMUL8: Claire.C@SIMUL8.com (Claire Cordeaux) Both Peter and Claire are active participants in a network called the Cumberland Initiative (www.cumberland-initiative.org/), which is a network of academics, clinicians and industry partners with the common goal of encouraging and facilitating the uptake of simulation and modelling approaches in healthcare.
Appendix 1 System Dynamics - Simulation of End of Life Care and Long Term Conditions The Whole Systems Partnership uses System Dynamics software to support strategy and partnership development across a range of health and social care services. These tools typically build on an assessment of local population needs to simulate change over time under different strategic scenarios. The development of the Cohort Model for End of Life Care needs was undertaken on behalf of the National End of Life Care Programme by a partnership of East Midlands SHA workforce team, Skills for Health, Skills for Care and the Whole Systems Partnership. The approach worked back from cause of death statistics to identify five ‘cohorts’ of need likely to be present within a given population during their last year of life. It then integrated this analysis with a functional analysis undertaken by Skills for Health to quantify the need for support at three different skill levels and in three broad time-frames, namely the skills required to realise early recognition of end of life care needs; the ongoing support required; and support during last days. A matrix of cohort of need and place of death was constructed to simulate the impact of early recognition on the nature and extent of support likely to be required in a given population, and to identify the likely impact on achieving greater numbers of people dying in their place of choice. The work has informed the costings work for end of life care and has provided an indication of the likely costs and savings in a given population as a result of enhanced early recognition of end of life care needs. Key findings from the modelling work included:
The current hospital costs for people whose admission ends in death is c.£1.5M per 100,000 population
That the number of deaths in a given population is not expected to rise up to 2020 except for people whose last year of life will be characterised by frailty where deaths will rise by c.9% in a typical population
At the time of the project less than 30% of people would have their end of life care needs recognised before their last few days
Once optimised, there is the potential for a recurrent net saving on hospital tariff less alternative community support of c£180k pa per 100,000 population.
The four locations who took part in early adopter projects for the modelling tool were able to identify their own equivalents for these high level estimates. This would be based on informed estimates of current levels of early recognition and their own demographics and death statistics. One location were able to use the outputs from the tool to argue successfully for the appointment of a community geriatrician with a special focus on end of life care in recognition of the increased needs of the frail elderly highlighted by the modelling tool. The Whole Systems Partnership work is actively involved in a range of other modelling work associated with long term conditions. It has worked with over a dozen health and social care partnerships in recent years to calibrate its Dementia Strategy Implementation Simulator, is Copyright Ethos Health Ltd 2013
working in other locations on modelling the needs of the frail elderly across health and social care and has undertaken modelling for people with Long Term Neurological Conditions. It is also a founding partner in the Ethos Partnership looking to deploy the benefits of system dynamics modelling more broadly in the field of Long Term Conditions (www.ethospartnership.com).
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Appendix 2 Scenario Generator - Simulation of Long Term Conditions & End of Life Care
SIMUL8 Corporation produces discrete event simulation software and co-developed a strategic planning simulation tool with the NHS Institute for Innovation and Improvement called Scenario Generator. Licenses were purchased for all commissioners and are still available for use. Scenario Generator is currently being used by seven pilot sites to simulate the QIPP Year of Care for Long Term Conditions. The simulation starts with the risk-stratified population, and their assessment of need, and dynamically models the flow of patients through different pathways of care depending on need and whether or not they are cared for by an integrated team. The simulation model allows users to change the health and social care services accessed by patients and the way in which they are used to reflect their local system. Data from the pilot sites enables the national and local teams to understand the different patterns of service use by similar types of patients, including the likelihood of emergency attendance and admission. It also includes the ability to test the impact of the new Rehabilitation, Recovery and Reablement Tariff. Users can create their own “virtual pathways” and experiment with “what if?” scenarios to understand the impact of any change in service on cost, volume of activity, resource utilization, waiting times and patient outcomes. The simulation is helping local and national teams to understand the financial implications of a Year of Care tariff for people with Long Term Conditions. During 2013, the final simulation model will be available to any NHS users with a Scenario Generator license, and an online model will also be available for those people who do not have access to a license. Scenario Generator has also been used to model End of Life Care, simulating the population likely to die in any given year through pathways of care and enabling users to test the impact of bringing in new interventions, such as shared electronic records between providers and a 24/7 Rapid Response Service in order to increase the likelihood of people being able to die outside of hospital. Results from the simulation are being used to inform the local strategy for End of Life Care and the business cases for investment in new service interventions. West Kent CCG will shortly be publishing a case study on their work in this area. One of the most interesting findings from the West Kent work was that the people most likely to die in hospital were ALSO the people most likely to die from a long term condition. For West Kent, the conclusion was that good care for people with long term conditions would also lead to good end of life care.
Copyright Ethos Health Ltd 2013